trigram-llm 0.1.0

Fast C-backed trigram language model for word prediction and sentence completion

trigram-llm 🧠

A fast, production-ready Python library for next-word prediction and sentence completion, powered by a hand-written C engine using a Prefix Trie, DJB2 HashMap, and Stupid Backoff smoothing.

Sub-millisecond predictions · Zero dependencies · Pure ctypes · Thread-safe

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Features

Feature	Description
train_from_text(text)	Train from any Python string
train_from_file(path)	Train from a text file (incremental)
train_from_list(words)	Train from a pre-tokenised word list
predict_next(w1, w2)	Greedy single-word prediction (< 1ms)
predict_top_n(w1, w2, n, temperature)	Top-N predictions with probabilities
complete_sentence(prompt, num_words, beam_width)	Beam search sentence generation
greedy_generate(prompt, num_words)	Fastest sentence completion
perplexity(text)	Evaluate model quality on held-out text
vocabulary()	Returns all known words as a Python set
get_stats()	Dict with trigram count, vocab size, etc.
save(path) / TrigramModel.load(path)	Binary model persistence
reset()	Clear model and retrain from scratch
"the quick" in model	Check if a bigram context was seen
len(model)	Total number of stored trigrams
Thread-safe	All predictions guarded by a threading.Lock
Context manager	with TrigramModel.load(path) as m:

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Installation

Prerequisites

• Python 3.8+
• GCC (macOS: xcode-select --install, Ubuntu: sudo apt install gcc)

Install (one command)

cd /path/to/Trigrams
pip install -e .

This compiles the C engine into trigram/_trigram_c.dylib (or .so on Linux) and installs the package in editable mode.

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Quickstart

from trigram import TrigramModel

# 1. Create and train
model = TrigramModel()
model.train_from_text("""
    The quick brown fox jumps over the lazy dog.
    The quick brown fox was nimble and swift.
    The lazy dog slept peacefully under the old oak tree.
""")

# 2. Predict next word (greedy)
word = model.predict_next("the", "quick")
print(word)  # → "brown"

# 3. Top-N predictions with probabilities
preds = model.predict_top_n("the", "quick", n=3, temperature=1.0)
# [{"word": "brown", "probability": 0.75, "count": 2},
#  {"word": "red",   "probability": 0.25, "count": 1}]

# 4. Sentence completion (beam search)
completions = model.complete_sentence("the quick", num_words=4, beam_width=3)
# [{"sentence": "the quick brown fox jumps", "probability": 0.012}, ...]

# 5. Greedy generation (fastest)
sentence = model.greedy_generate("the quick", num_words=3)
# "the quick brown fox"

# 6. Evaluate quality
ppl = model.perplexity("the quick brown fox")
print(f"Perplexity: {ppl:.2f}")

# 7. Inspect model
print(len(model))          # → total trigrams
print("the quick" in model)  # → True
print(model.vocabulary())  # → {"the", "quick", "brown", ...}
print(model.get_stats())   # → {"total_trigrams": 7, "unique_first_words": 3, ...}

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Training from a File

model = TrigramModel()
model.train_from_file("path/to/my_corpus.txt")

# Incremental training — add more data later
model.train_from_file("path/to/more_data.txt")

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Saving and Loading Models

# Save
model.save("my_model.bin")

# Load (class method)
model2 = TrigramModel.load("my_model.bin")

# Context manager (auto-frees on exit)
with TrigramModel.load("my_model.bin") as m:
    print(m.predict_next("the", "quick"))

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Temperature Sampling

The temperature parameter controls how creative predictions are:

# Deterministic — always picks the most common word
model.predict_top_n("the", "quick", temperature=0.1)

# Standard probability distribution
model.predict_top_n("the", "quick", temperature=1.0)

# More diverse / creative
model.predict_top_n("the", "quick", temperature=2.0)

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Advanced Usage

Train from a word list (custom tokenisation)

import nltk
tokens = nltk.word_tokenize("The quick brown fox")
tokens = [t.lower() for t in tokens if t.isalpha()]

model = TrigramModel()
model.train_from_list(tokens)

Thread-safe batch prediction

import threading

def worker(model, results, idx):
    results[idx] = model.predict_top_n("the", "quick", n=5)

model = TrigramModel.load("model.bin")
results = [None] * 10
threads = [threading.Thread(target=worker, args=(model, results, i)) for i in range(10)]
for t in threads: t.start()
for t in threads: t.join()

Check if a context exists before predicting

if "the quick" in model:
    result = model.predict_next("the", "quick")

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API Reference

TrigramModel()

Creates a new empty model.

train_from_text(text: str) → int

Train on a raw text string. Returns trigrams inserted.

train_from_file(path) → int

Train from a text file. Returns trigrams inserted.

train_from_list(words: list) → int

Train from a pre-tokenised word list. Returns trigrams inserted.

predict_next(w1, w2) → str | None

Return the single most-likely next word or None.

predict_top_n(w1, w2, n=5, temperature=1.0) → list[dict]

Return up to N predictions sorted by probability descending. Each dict: {"word": str, "probability": float, "count": int}.

complete_sentence(prompt, num_words=5, beam_width=3) → list[dict]

Generate sentence completions via beam search. Each dict: {"sentence": str, "probability": float}.

greedy_generate(prompt, num_words=5) → str

Fastest sentence completion using greedy decoding.

perplexity(text) → float

Compute per-token perplexity on held-out text. Lower = better.

vocabulary() → set[str]

All words seen in the first-word position of training trigrams.

get_stats() → dict

{"total_trigrams": int, "unique_first_words": int, "vocabulary_size": int}.

save(path) → None

Save model to binary file. Compatible with the C CLI tool.

TrigramModel.load(path) → TrigramModel (classmethod)

Load a pre-trained binary model. Supports context manager protocol.

reset() → None

Clear all training data.

len(model) → int

Total stored trigrams.

"w1 w2" in model / ("w1", "w2") in model → bool

Check if a bigram context exists.

repr(model)

TrigramModel(trigrams=11,062,203, vocab=97,277)

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Performance

Operation	Latency
Single word prediction	< 1ms
Top-5 predictions	1–2ms
Beam search (5 words, width 3)	5–10ms
Training (1M words)	~30s

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Running Tests

pip install pytest
pytest tests/ -v

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Project Structure

Trigrams/
├── trigram/                  # Python library
│   ├── __init__.py
│   ├── _lib.py               # ctypes bindings
│   ├── model.py              # TrigramModel class
│   ├── utils.py              # Text preprocessing
│   └── _trigram_c.dylib      # Compiled C engine (auto-generated)
├── trigram_llm/
│   ├── src/                  # C source files
│   └── include/              # C headers
├── tests/                    # pytest test suite
├── setup.py                  # Build script
└── pyproject.toml

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License

MIT License — feel free to use, modify, and distribute.